GUIM‐SMD: guilty user identification model using summation matrix‐based distribution
Ishu Gupta, Ashutosh Kumar Singh
Abstract
Data sharing across multiple different entities is on‐demand to upgrade an enterprise's performance. However, some malicious entity can reveal this data to an unauthorised third party that may result in heavy loss to the enterprises in terms of finance, reputation, and long‐term stability. This study presents a novel model GUIM‐SMD for the identification of the guilty entity which is responsible for the data leakage to the unauthorised party in the shared environment. An effective distribution strategy to allocate the data among the users based on the access control mechanism is proposed in this model. The approach introduces the summation matrix which is computed using D‐score and U‐score that are assigned to the classified data and user, correspondingly. Furthermore, D‐score and U‐score are based on the data sensitivity and user guilty record relatively; and their values vary between 0 and 1. The evaluated summation matrix is used for data distribution among various users. The results show improvement up to 98.74, 236.38, and 252.39% for average probability, average success rate, and detection rate, respectively, as compared to the prior work.